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keras/
qwen2.5_coder_instruct_0.5b

Name: qwen2.5_coder_instruct_0.5b
Availability: InStock

keras-hub

text-generation

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Model Overview

Model Summary

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:

Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.

A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.

Long-context Support up to 128K tokens.

For more details, please refer to Qwen Blog, GitHub, and Documentation.

Weights are released under the Apache 2 License . Keras model code is released under the Apache 2 License.

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.

Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.

Preset name	Parameters	Description
qwen2.5_coder_0.5b	0.5B	24-layer with 0.5 billion parameters. Code-Specific large language models base on the strong Qwen2.5
qwen2.5_coder_instruct_0.5b	0.5B	24-layer with 0.5 billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_1.5b	1.5B	28-layer with 1.5 billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_instruct_1.5b	1.5B	28-layer with 1.5 billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_3b	3B	36-layer with 3 billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_instruct_3b	3B	36-layer with 3 billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_7b	7B	28-layer with 7B billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_instruct_7b	7B	28-layer with 7B billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_14b	14B	48-layer with 14B billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_instruct_14b	14B	48-layer with 14B billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_32b	32B	64-layer with 32B billion parameters. Code-Specific large language models base on the strong Qwen2.5.
qwen2.5_coder_instruct_32b	32B	64-layer with 32B billion parameters. Code-Specific large language models base on the strong Qwen2.5.

Example Usage

Python

import keras
import keras_hub
import numpy as np

# Use generate() to do code generation.
qwen_lm = keras_hub.models.QwenCausalLM.from_preset("qwen2.5_coder_instruct_0.5b")
qwen_lm.generate(" write a quick sort algorithm in python.", max_length=512)

Example Usage with Hugging Face URI